Integrating Multiple Inferences for Vehicle Detection by Focusing on Challenging Test Sets

摘要

Due to recent advances in object detection with the help of deep convolutional neural networks and region proposal methods, object detection systems have become practical in numerous fields with high accuracy. This paper presents a method for vehicle detection in videos for automatic traffic monitoring. Compared to general object detection datasets such as the PascalVOC and MS-COCO, traffic surveillance datasets such as the UA-DETRAC dataset have different challenging issues: high variation of object size, severe occlusion, dissimilarity between training set and test set. To overcome these difficulties, we employ an unsupervised integration of multiple instances of an image by analyzing video sequences. We applied Faster R-CNN with Neural Architecture Search (NAS) framework as a base network. We achieved 85.76% mAP on the UA-DETRAC detection test set, and outperformed the winner method of the AVSS 2017 challenge on Advanced Traffic Monitoring by 9.19%.